This record provides the processed data and Version 1.0 of the analysis code supporting the study “Enhancing Urban Flood Risk Assessment: A PCA-Integrated Deep Learning Surrogate for Hazard and Damage Prediction.” The archive includes the synthetic rainfall–inundation–damage database, observed-event rainfall descriptors and reference inundation maps, data-split information, model artifacts, evaluation results, and Python scripts used for PCA-IDNN training, real-event evaluation and refinement, and PCA-latent-assisted damage prediction. Proprietary InfoWorks ICM and K-FRM files and associated drainage-network, asset, and restricted GIS data are not included because of commercial licensing and institutional data restrictions.
Presentation Topic: ServiceNow Enterprise Risk Management (ERM): Architecture of an Intelligent Governance, Risk, and Compliance (GRC) / Integrated Risk Management (IRM) and Smart Assessment Engine The submitted materials include Mr. Chanthati's keynote speaker profile, conferenc…
Abstract The rapid growth of big data and the increasing complexity of deep learning applications have created significant challenges for traditional data processing infrastructures, particularly in terms of scalability, performance, and resource efficiency. This study presents a…
This repository contains the datasets and source code associated with Georgiev et al., Science Advances (2026). https://doi.org/10.1126/sciadv.aec5080 To get started with the software, visit our GitHub repository. The software provides both a graphical user interface (GUI) and a…
Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment" This repository contains the necessary codes to reproduce results in the paper: Baals, L. J., Liu, Y., Osterrieder, J., & Hadji-Misheva, B. (2025). A Syste…
This study aims to evaluate the implementation of the deep learning approach in Physical Education, Sports, and Health (PJOK) learning in public junior high schools in Godean District, based on the Countenance Stake Evaluation Model, which includes antecedents, transactions, and…
## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Analysis of Cube {100}<001> and Goss {110}<001> Textures: Machine Learning, Deep Learning, and Generative Models for Crystallographic Texture Quantification in Metallurgical Engineering"** ### Alternative T…